code: def plot_interval_estimates(
      point_estimates,
      interval_estimates,
      metric_names,
      algorithms=None,
      colors=None,
      color_palette="colorblind",
      max_ticks=4,
      subfigure_width=3.4,
      row_height=0.37,
      xlabel_y_coordinate=-0.1,
      xlabel="Normalized Score",
      **kwargs,
  ):
      """Plots various metrics with confidence intervals.
  
      Args:
        point_estimates: Dictionary mapping algorithm to a list or array of point
          estimates of the metrics to plot.
        interval_estimates: Dictionary mapping algorithms to interval estimates
          corresponding to the `point_estimates`. Typically, consists of stratified
          bootstrap CIs.
        metric_names: Names of the metrics corresponding to `point_estimates`.
        algorithms: List of methods used for plotting. If None, defaults to all the
          keys in `point_estimates`.
        colors: Maps each method to a color. If None, then this mapping is created
          based on `color_palette`.
        color_palette: `seaborn.color_palette` object for mapping each method to a
          color.
        max_ticks: Find nice tick locations with no more than `max_ticks`. Passed to
          `plt.MaxNLocator`.
        subfigure_width: Width of each subfigure.
        row_height: Height of each row in a subfigure.
        xlabel_y_coordinate: y-coordinate of the x-axis label.
        xlabel: Label for the x-axis.
        **kwargs: Arbitrary keyword arguments.
  
      Returns:
        fig: A matplotlib Figure.
        axes: `axes.Axes` or array of Axes.
      """
  
      if algorithms is None:
          algorithms = list(point_estimates.keys())
      num_metrics = len(point_estimates[algorithms[0]])
      figsize = (
          subfigure_width * num_metrics,
          row_height * len(algorithms),
      )
      fig, axes = plt.subplots(
          nrows=1, ncols=num_metrics, figsize=figsize
      )
      if colors is None:
          color_palette = sns.color_palette(
              color_palette, n_colors=len(algorithms)
          )
          colors = dict(zip(algorithms, color_palette))
      h = kwargs.pop("interval_height", 0.6)
  
      for idx, metric_name in enumerate(metric_names):
          for alg_idx, algorithm in enumerate(algorithms):
              ax = axes[idx] if num_metrics > 1 else axes
              # Plot interval estimates.
              lower, upper = interval_estimates[algorithm][:, idx]
              ax.barh(
                  y=alg_idx,
                  width=upper - lower,
                  height=h,
                  left=lower,
                  color=colors[algorithm],
                  alpha=0.75,
                  label=algorithm,
              )
              # Plot point estimates.
              ax.vlines(
                  x=point_estimates[algorithm][idx],
                  ymin=alg_idx - (7.5 * h / 16),
                  ymax=alg_idx + (6 * h / 16),
                  label=algorithm,
                  color="k",
                  alpha=0.5,
              )
  
          ax.set_yticks(list(range(len(algorithms))))
          ax.xaxis.set_major_locator(plt.MaxNLocator(max_ticks))
          if idx != 0:
              ax.set_yticks([])
          else:
              ax.set_yticklabels(algorithms, fontsize="x-large")
          ax.set_title(metric_name, fontsize="xx-large")
          ax.tick_params(axis="both", which="major")
          _decorate_axis(ax, ticklabelsize="xx-large", wrect=5)
          ax.spines["left"].set_visible(False)
          ax.grid(True, axis="x", alpha=0.25)
      fig.text(
          0.4,
          xlabel_y_coordinate,
          xlabel,
          ha="center",
          fontsize="xx-large",
      )
      plt.subplots_adjust(wspace=kwargs.pop("wspace", 0.11), left=0.0)
      return fig, axes
  
file_path: rliable/rliable/plot_utils.py
imports: import matplotlib.pyplot
  import numpy
  import seaborn
label_desc: Use image node_id 5 for calling a global function with example usage: plot_interval_estimates(point_estimates, interval_estimates, metric_names, algorithms, colors, color_palette, max_ticks, subfigure_width, row_height, xlabel_y_coordinate, xlabel) and returns: fig,  axes